System and method for radar cross traffic tracking and maneuver risk estimation

ABSTRACT

A risk maneuver assessment system and method to generate a perception of an environment of a vehicle and a behavior decision making model for the vehicle; a sensor system configured to provide the sensor input in the environment for filtering target objects; one or more modules configured to map and track target objects to make a candidate detection from multiple candidate detections of a true candidate detection as the tracked target object; apply a Markov Random Field (MRF) algorithm for recognizing a current situation of the vehicle and predict a risk of executing a planned vehicle maneuver at the true detection of the dynamically tracked target; apply mapping functions to sensed data of the environment for configuring a machine learning model of decision making behavior of the vehicle; and apply adaptive threshold to cells of an occupancy grid for representing an area of tracking of objects within the vehicle environment.

TECHNICAL FIELD

The present disclosure generally relates to pre-decision maneuver riskplanning, and more particularly relates to systems and methods in avehicle for generating fuller representations of uncertainty indicatorsfor use in pre-decision maneuver risk planning under conditions ofreduced sensed information.

Vehicle perception systems have been introduced into vehicles to allow avehicle to sense its environment and in some cases to allow the vehicleto navigate autonomously or semi-autonomously. Sensing devices that maybe employed in vehicle perception systems include radar, lidar, imagesensors, and others.

While recent years have seen significant advancements in vehicleperception systems, such systems still warrant improvements in a numberof respects. That is, radars, particularly those used in automotiveapplications, can provide data with significant detection, velocity andlocation uncertainties. That is, the radars under conditions, mayoperate in instances with radar scan detections producing reducedamounts of information and return results that prevent or fail, whenused as the basis for further processing steps, from achieving a neededaccuracy for further required vehicle detection operations of targetobject tracking for maneuver planning operations.

Accordingly, it is desirable to provide improved systems, apparatus andmethods for pre-decision (not yet decided) maneuver risk planning ofvehicles by providing fuller representations of uncertainty indicatorsfrom results of radar scan detections (or the like) about the vehicleresulting in reduced amounts of sensed data that can result inincomplete representations of the uncertainties used to determinemaneuver risk estimations in vehicle maneuvers.

Furthermore, other desirable features and characteristics of the presentinvention will become apparent from the subsequent detailed descriptionand the appended claims, taken in conjunction with the accompanyingdrawings and the foregoing technical field and background.

SUMMARY

Systems and methods for an enhanced representation of uncertainty inradar detections for maneuver risk assessments are provided.

In one embodiment, a risk maneuver assessment system for planningmaneuvers with uncertainties of a vehicle is provided. The systemincludes: a first controller with a processor programmed to generate aperception of an environment of the vehicle and a behavior decisionmaking model for the vehicle including performing a calculation upon asensor input to provide, as an output an action risk mapping and atleast one target object tracking for different areas within theenvironment of the vehicle; a sensor system configured to provide thesensor input to the processor for providing an area in the environmentof vehicle for filtering target objects; one or more modules configuredto, by a processor, map and track target objects to make a candidatedetection from multiple candidate detections of a true candidatedetection as the tracked target object; one or more first modulesconfigured to, by the processor, apply a Markov Random Field (MRF)algorithm for recognizing a current situation of the vehicle in theenvironment and predict a risk of executing a planned vehicle maneuverat the true detection of the dynamically tracked target; one or moresecond modules configured to, by the processor, apply mapping functionsto sensed data of the environment for configuring a machine learningmodel of decision making behavior of the vehicle; one or more thirdmodules configured to, by the processor, apply adaptive threshold tocells of an occupancy grid configured for representing an area oftracking of objects within the vehicle environment; and a secondcontroller with a processor configured to generate control commands inaccordance with modeling of the decision making behavior and theperception of the environment of the vehicle for planned vehiclemaneuvers.

In various exemplary embodiments, the system further includes: the oneor more first modules programmed to generate a Markov Random Field (MRF)to recognize the current situation. The system, further includes: theone or more of the second and/or third modules further configured toselect as the true detection at least one of the candidate detectionsthat is within a radius for the target, and a candidate detection thatis closest to a first known mapped pathway. The system, furtherincludes: the one or more of the second and/or third modules furtherconfigured to select as the true detection, the candidate that indicatesa position and velocity that are consistent with a target traveling on asecond known travel mapped pathway, and a selection as a falsedetection, the candidate that indicates a position that is outside thesecond known pathway or the velocity is not consistent with the targettraveling.

The system, further includes: a fourth module configured to compute, bya gating operation, a distance metric from the last position of atracked target object to a predicted position less than a thresholddistance related to one or more of the candidate detections. The system,further includes: one or more fifth modules configured to, by theprocessor, apply the Markov Random Field (MRF) algorithm representingthe tracked target object in one or more cells of an occupancy grid by:calculating an object measurement density for each tracked target objectrepresented in the one or more cells of the occupancy grid; spreadingthe density over a window including the set of cells of the occupancygrid represented by the tracked target object; spreading velocities overthe window including a same set of cells of the occupancy gridrepresented by the tracked target object.

The system, further includes: one or more sixth modules configured to,by the processor, apply mapping functions to sensed data of theenvironment for configuring a machine learning (ML) model of decisionmaking behavior of the vehicle by an action risk assessment modeltrained using semi-supervised machine learning techniques by on-line andoff-line training for mapping function to candidate actions to determinewith risk factors a learned drivable path. The system, further includes:a seventh module configured to, by a processor, perform in the off-linetraining of the ML model including: collecting labels, co-collectingoccupancy velocity grids, extracting features from the occupancy grids,and applying at least support vector machine (SVM) techniques forrecognizing class patterns of the candidate actions to determine withrisk factors the learned drivable path.

The system, further includes: an eighth module configured to, by theprocessor, apply adaptive threshold to cells of an occupancy gridconfigured for representing area of tracking of objects within thevehicle environment including: a ninth module configured to, by theprocessor, compute by an adaptive threshold occupancy density, thelikelihood that a candidate action is available for the target trackedobject based on the computed density distribution, and select thecandidate action that has the highest probability of being available;and a tenth module configured to, by the processor, compute a clusteringfor velocity clusters for a set of candidate actions to select thetarget tracked object that indicates a position that is consistent witha learned drivable path. The ML model is trained using reinforcementlearning techniques using a data set of past collected labels and sensordata of drivable paths and wherein the eight module is configured toselect the candidate action that will likely contribute to one of thedrivable paths wherein the sensor data at least includes one of: radar,acoustic, lidar or image sensor data.

In another exemplary embodiment, a vehicle, including: a sensordetection sensing device including one or more of a set including: aradar, acoustic, lidar and image sensing device; a risk maneuverassessment system for assessing one or more uncertainty factors inplanned maneuvers; and a plurality of modules configured to, by aprocessor, generate a perception of an environment of the vehicle and aoutput target output for tracking different areas within the environmentis provided. The vehicle includes: the plurality of modules including:one or more modules configured to, by a processor, map and track targetobjects to make a candidate detection from multiple candidate detectionsof a true candidate detection as the tracked target object; one or moremodules configured to, by the processor, apply a Markov Random Field(MRF) algorithm for recognizing a current situation of the vehicle inthe environment and for predicting a risk of executing a planned vehiclemaneuver at the true detection of the dynamically tracked target; one ormore modules configured to, by the processor, apply mapping functions tosensed data of the environment for configuring a machine learning modelof decision making behavior of the vehicle; one or more modulesconfigured to, by the processor, apply adaptive threshold to cells of anoccupancy grid configured for representing areas of tracking of objectswithin the environment; and a controller with a processor configured togenerate control commands in accordance with modeling of the decisionmaking behavior and the perception of the environment of the vehicle forplanned vehicle maneuvers.

In various exemplary embodiments, the one or more modules are programmedto generate a Markov Random Field (MRF) to recognize the currentsituation. The system, further includes: the one or more modulesconfigured to select as the true detection including: a first moduleconfigured to select, as the true detection, the candidate detectionthat is within a radius for the target; and a second module configuredto select, as the true detection, the candidate detection that isclosest to a first known mapped pathway.

The system, further includes: the one or more modules further configuredto select as the true detection including: a third module configured toselect the true detection, the candidate that indicates a position andvelocity that are consistent with a target traveling on a second knowntravel mapped pathway; and the third module configured to select a falsedetection, the candidate that indicates a position that is outside thesecond known pathway or the velocity is not consistent with the targettraveling.

The system, further includes: the one or more modules further configuredto select as the true detection including: a fourth module configured tocompute, by a gating operation, a distance metric from the last positionof a tracked target object to a predicted position less than a thresholddistance related to one or more of the candidate detections. The one ormore modules configured by the processor for applying the Markov RandomField (MRF) algorithm representing the tracked target object in one ormore cells of an occupancy grid further including: a fifth module isconfigured to calculate an object measurement density for each trackedtarget object represented in the one or more cells of the occupancygrid; a sixth module is configured to spread the density over a windowincluding the set of cells of the occupancy grid represented by thetracked target object; a seventh module is configured to spreadvelocities over the window including a same set of cells of theoccupancy grid represented by the tracked target object.

The the one or more modules configured by the processor for applyingmapping functions to sensed data of the environment for configuring amachine learning (ML) model of decision making behavior of the vehiclefurther including: an eighth module including an action risk assessmentmodel trained using semi-supervised machine learning techniques byon-line and off-line training for mapping function to candidate actionsto determine with risk factors a learned drivable path wherein the eightmodule in the off-line training of the ML model includes: collectinglabels, co-collecting occupancy velocity grids, extracting features fromthe occupancy grids, and applying at least support vector machine (SVM)techniques for recognizing class patterns of the candidate actions todetermine with risk factors the learned drivable path.

The one or more modules for applying adaptive threshold to cells of anoccupancy grid configured for representing area of tracking of objectswithin the vehicle environment including: a ninth module configured tocompute by an adaptive threshold occupancy density, the likelihood thata candidate action is available for the target tracked object based onthe computed density distribution, and select the candidate action thathas the highest probability of being available; and a tenth moduleconfigured to compute a clustering for velocity clusters for a set ofcandidate actions to select the target tracked object that indicates aposition that is consistent with a learned drivable path. The ML modelis trained using reinforcement learning techniques using a data set ofpast collected labels and radar data of drivable paths and wherein theeight module is configured to select the candidate action that willlikely contribute to one of the drivable paths.

In yet another embodiment, a planning system of a vehicle is provided.The system includes: a sensor system configured to provide the sensorinput to the processor for providing an area in the environment ofvehicle for filtering target objects; and a non-transitory computerreadable medium including: a first module configured to, by a processor,select, as the true detection, the candidate detection that is within aradius for the target; a second module configured to, by a processor,select, as the true detection, the candidate detection that is closestto a first known mapped pathway; a third module configured to, by aprocessor, select the true detection, the candidate that indicates aposition and velocity that are consistent with a target traveling on asecond known travel mapped pathway, and the third module configured toselect a false detection, the candidate that indicates a position thatis outside the second known pathway or the velocity is not consistentwith the target traveling; a fourth module configured to, by aprocessor, compute, by a gating operation, a distance metric from thelast position of a tracked target object to a predicted position lessthan a threshold distance related to one or more of the candidatedetections; a fifth module is configured to, by a processor, calculatean object measurement density for each tracked target object representedin the one or more cells of the occupancy grid; a sixth module isconfigured to, by a processor, spread the density over a windowincluding the set of cells of the occupancy grid represented by thetracked target object; a seventh module is configured to, by aprocessor, spread velocities over the window including a same set ofcells of the occupancy grid represented by the tracked target object; aneighth module including an action risk assessment model trained usingsemi-supervised machine learning techniques by on-line and off-linetraining for mapping function to candidate actions to determine withrisk factors a learned drivable path wherein the eighth module in theoff-line training of the ML model is configured to, by a processor,collect labels, co-collect occupancy velocity grids, extract featuresfrom the occupancy grids, and apply at least support vector machine(SVM) techniques for recognizing class patterns of the candidate actionsto determine with risk factors the learned drivable path; a ninth moduleconfigured to, by a processor, compute by an adaptive thresholdoccupancy density, the likelihood that a candidate action is availablefor the target tracked object based on the computed densitydistribution, and select the candidate action that has the highestprobability of being available; and a tenth module configured to, by aprocessor, compute a clustering for velocity clusters for a set ofcandidate actions to select the target tracked object that indicates aposition that is consistent with a learned drivable path wherein the MLmodel is trained using reinforcement learning techniques using a dataset of past collected labels and radar data of drivable paths andwherein the eight module is configured to select the candidate actionthat will likely contribute to one of the drivable paths.

DESCRIPTION OF THE DRAWINGS

The exemplary embodiments will hereinafter be described in conjunctionwith the following drawing figures, wherein like numerals denote likeelements, and wherein:

FIG. 1 depicts an example vehicle that includes a radar detection riskmaneuver planning module using fuller representations of uncertaintyindicators from sensed radar data, in accordance with variousembodiments;

FIG. 2 is a functional block diagram illustrating an autonomous drivingsystem (ADS) associated with an autonomous vehicle, in accordance withvarious embodiments;

FIG. 3 is a block diagram depicting an example radar detections maneuverrisk planning module for use in a vehicle, in accordance with variousembodiments;

FIG. 4 is a diagram depicting an example illustration of the use of anexample pre-filtering module with track gating to select the more likelytrue detection between multiple radar candidate detections received fromradar sensors, in accordance with various embodiments;

FIG. 5 is a diagram depicting an example illustration of the use of anexample Markov Random Field (MRF) Module for generating a belief-stateMarkov model to track the uncertainties over cross-traffic detections,in accordance with various embodiments;

FIG. 6 is a diagram depicting an example illustration of the use of anexample action risk assessment module that by semi-supervised MachineLearning (ML) modeling and training uses path plan data to select themore likely true action candidate between multiple action candidatedetections to predict drivable paths, in accordance with variousembodiments;

FIG. 7 is a diagram depicting an example illustration of the use of anexample object extraction for object tracking using adaptive thresholdoccupancy grid density cells, in accordance with various embodiments;and

FIG. 8 is a process flow chart depicting an example process for maneuverrisk assessments of uncertainties to predict drivable paths based onmultiple radar candidate detections, in accordance with variousembodiments.

DETAILED DESCRIPTION

The following detailed description is merely exemplary in nature and isnot intended to limit the application and uses. Furthermore, there is nointention to be bound by any expressed or implied theory presented inthe preceding technical field, background, summary, or the followingdetailed description. As used herein, the term “module” refers to anyhardware, software, firmware, electronic control component, processinglogic, and/or processor device, individually or in any combination,including without limitation: application specific integrated circuit(ASIC), a field-programmable gate-array (FPGA), an electronic circuit, aprocessor (shared, dedicated, or group) and memory that executes one ormore software or firmware programs, a combinational logic circuit,and/or other suitable components that provide the describedfunctionality.

Embodiments of the present disclosure may be described herein in termsof functional and/or logical block components and various processingsteps. It should be appreciated that such block components may berealized by any number of hardware, software, and/or firmware componentsconfigured to perform the specified functions. For example, anembodiment of the present disclosure may employ various integratedcircuit components, e.g., memory elements, digital signal processingelements, logic elements, look-up tables, or the like, which may carryout a variety of functions under the control of one or moremicroprocessors or other control devices. In addition, those skilled inthe art will appreciate that embodiments of the present disclosure maybe practiced in conjunction with any number of systems, and that thesystems described herein is merely exemplary embodiments of the presentdisclosure.

Autonomous vehicles, which operate in complex dynamic environments,require methods that generalize to unpredictable situations and reasonin a timely manner in order to reach a level reliability and reactsafely even in complex urban situations. Informed decisions requireaccurate perception. However, current computer vision systems have yetto achieve error rates acceptable for autonomous navigation. Bycombining decision-making, control, and perception with machine learningtechniques and complex planning and decision-making methods as describedherein can be a viable option.

For the sake of brevity, conventional techniques related to signalprocessing, data transmission, signaling, control, machine learningmodels, radar, lidar, image analysis, and other functional aspects ofthe systems (and the individual operating components of the systems) maynot be described in detail herein. Furthermore, the connecting linesshown in the various figures contained herein are intended to representexample functional relationships and/or physical couplings between thevarious elements. It should be noted that many alternative or additionalfunctional relationships or physical connections may be present in anembodiment of the present disclosure.

The subject matter described herein discloses apparatus, systems,techniques and articles for operating maneuver control and planningsystems of a vehicle. The described apparatus, systems, techniques andarticles are associated with a sensor system of a vehicle as well as acontroller for receiving inputs from one or more sensing devices of thesensor system with levels of uncertainty for use in determining,planning, predicting, and/or performing vehicle maneuvers in real-timeor in the near future, or in the future. To this end, the controller mayemploy at least one adaptive algorithm utilizing indicators ofuncertainty with limited sensor data available and/or on a priori sensordata.

The sensing devices may include a combination of sensors of differentoperational modalities for gathering a variety of sensor data. Forexample, the sensing devices may include one or more cameras as well asradar-based or laser-based sensing devices (e.g., lidar sensingdevices). That is while the disclosure describing in detail radar basedscan detections, it is contemplated that the disclosure should not be solimiting an encompass a variety of different sensing devices usedsingularly or in combination such as sensing devices including acoustic,Lidar, image and IR sensor type devices.

During operation, the sensor system may gather sensor data, which isreceived by a processor of the controller. The processor may beprogrammed to convert the sensor data into a perception (i.e., belief)model with uncertainty factors about the vehicle and/or its environmentfor either decision or pre-decision maneuver planning. For example, theprocessor may determine where surrounding vehicles are located inrelation to the subject vehicle, predict the path of surroundingvehicles, determine and track the current vehicle path, recognizepavement markings, locate pedestrians and cyclists and predict theirmovements, and predict with representations of uncertainties ofdecisions to be made for future vehicle maneuvers and more.

In some embodiments, the processor generates an occupancy grid with aplurality of cells that collectively represent the perceived environmentof the vehicle. The processor calculates at least one perception datumfor the different cells within the occupancy grid. The perception datumrepresents a perceived element of the vehicle's environment. Theprocessor generates a representation of an uncertainty (i.e. anuncertainty factor) for the different cells, wherein the uncertaintyrepresentation can be indicative of the processor's uncertainty aboutthe perception and future maneuver associated with the cell as a meansfor fuller representation and taking into account factors of thisuncertainty. The perception data and uncertainty factors may becalculated from the sensor output received for the vehicle sensors usingone or more estimation applications such as Bayesian, Markov or otherstatistical algorithms.

The perception (i.e. representation of the uncertainty) as well as theindividual uncertainty factors included in the cells of the grid may beupdated continuously as the vehicle operates. Additionally, theprocessor determines situational relevance of the different cells withinthe occupancy grid. Relevance may be determined in various ways.

The apparatus, systems, methods, disclosed herein may utilize resultsfrom other sensors that are present in a vehicle (e.g. cameras, otherradars) to enable a more full representation of uncertainty to determinepre-decision maneuver risk indicators, which avoids the problem ofreduced information due to the representation incurred in the vehiclemaneuver planning.

In various exemplary embodiments, the decision indicators can still becombined together with other pre-decision indicators to achieve betterplanning under the uncertainty.

In various exemplary embodiments, the apparatus, systems, methods,described can be combined and enhance results from back-end processingmethods providing data products to perception fusion via arepresentation to achieve more accurate maneuver planning underuncertainty.

The apparatus, systems, methods, disclosed herein uses a more fullrepresentation of uncertainty to determine pre-decision maneuver riskindicators, which avoids the problem of reduced information due torepresentation. Further, the pre-decision indicators can still becombined with other multiple approaches and/or other pre-decisionindicators and planning goals to achieve better planning underuncertainty.

In various embodiments, the present disclosure provides apparatus,systems, methods, using an occupancy-velocity belief-state density gridand a belief-state Markov model to track the uncertainty overcross-traffic detection and awareness.

In various embodiments, the present disclosure provides apparatus,systems and methods that perform supervised, unsupervised and estimationmodeling that learns and predicts by enhanced uncertaintyrepresentations, the risk of performing certain maneuvers such asmerging into traffic at an intersection with and without extractingconventional object tracks for readily fusing sensor data with outputsof other sensors (e.g. cameras, Lidars) of the vehicle.

In various embodiments, the present disclosure provides apparatus,systems and methods for vehicle maneuvers of: merging into traffic,planning merging maneuvers under high sensing of uncertainty, handlingof uncertainty more robustly, of complex (multimode) vehicle presencedistribution estimations, and of conveying higher complexity informationdirectly to discriminative pre-decision data products.

In various embodiments, the present disclosure provides apparatus,systems and methods for vehicle maneuvers: of multiple approaches ofvarying complexity, of reinforcement learning of maneuver risks, ofoccupancy-velocity grid Markov model tracking, of utilizing additionalsensors present or in standalone, and of integrating with existingtraditional tracking and fusion systems.

In various embodiments, the present disclosure provides apparatus,systems and methods for maneuver risk planning over specific regions ofinterest and only for the current belief state by a look-ahead in thebelief state space to determine only those belief states that arereachable from the current state.

Instead of keeping perception modules and risk maneuver planning modulesseparate, an alternative framework is described herein to train certainparts of the perception module to incorporate partial tasks from therisk maneuver planning module.

FIG. 1 depicts an example vehicle 100 that includes a cross traffictracking and maneuver risk estimation planning module for generating afuller representation of uncertainty indicators 302 (hereinafter“maneuver risk planning module”) from sensed data from a radar system(and/or other sensors). As depicted in FIG. 1, the vehicle 100 generallyincludes a chassis 12, a body 14, front wheels 16, and rear wheels 18.The body 14 is arranged on the chassis 12 and substantially enclosescomponents of the vehicle 100. The body 14 and the chassis 12 mayjointly form a frame. The wheels 16-18 are each rotationally coupled tothe chassis 12 near a respective corner of the body 14.

In various embodiments, the vehicle 100 may be an autonomous vehicle ora semi-autonomous vehicle. An autonomous vehicle 100 is, for example, avehicle that is automatically controlled to carry passengers from onelocation to another. The vehicle 100 is depicted in the illustratedembodiment as a passenger car, but other vehicle types, includingmotorcycles, trucks, sport utility vehicles (SUVs), recreationalvehicles (RVs), marine vessels, aircraft, etc., may also be used.

As shown, the vehicle 100 generally includes a propulsion system 20, atransmission system 22, a steering system 24, a brake system 26, asensor system 28, an actuator system 30, at least one data storagedevice 32, at least one controller 34, and a communication system 36.The propulsion system 20 may, in various embodiments, include aninternal combustion engine, an electric machine such as a tractionmotor, and/or a fuel cell propulsion system. The transmission system 22is configured to transmit power from the propulsion system 20 to thevehicle wheels 16 and 18 according to selectable speed ratios. Accordingto various embodiments, the transmission system 22 may include astep-ratio automatic transmission, a continuously-variable transmission,or other appropriate transmission.

The brake system 26 is configured to provide braking torque to thevehicle wheels 16 and 18. Brake system 26 may, in various embodiments,include friction brakes, brake by wire, a regenerative braking systemsuch as an electric machine, and/or other appropriate braking systems.

The steering system 24 influences a position of the vehicle wheels 16and/or 18. While depicted as including a steering wheel 25 forillustrative purposes, in some embodiments contemplated within the scopeof the present disclosure, the steering system 24 may not include asteering wheel.

The sensor system 28 includes one or more sensing devices 40 a-42 n thatsense observable conditions of the exterior environment and/or theinterior environment of the vehicle 100 (such as the state of one ormore occupants) and generate sensor data relating thereto. Sensingdevices 40 a-42 n might include, but are not limited to, radars (e.g.,long-range, medium-range-short range), lidars, global positioningsystems, optical cameras (e.g., forward facing, 360-degree, rear-facing,side-facing, stereo, etc.), thermal (e.g., infrared) cameras, ultrasonicsensors, odometry sensors (e.g., encoders) and/or other sensors thatmight be utilized in connection with systems and methods in accordancewith the present subject matter.

The actuator system 30 includes one or more actuator devices 40 a-42 nthat control one or more vehicle features such as, but not limited to,the propulsion system 20, the transmission system 22, the steeringsystem 24, and the brake system 26. In various embodiments, vehicle 100may also include interior and/or exterior vehicle features notillustrated in FIG. 1, such as various doors, a trunk, and cabinfeatures such as air, music, lighting, touch-screen display components(such as those used in connection with navigation systems), and thelike.

The data storage device 32 stores data for use in automaticallycontrolling the vehicle 100. In various embodiments, the data storagedevice 32 stores defined maps of the navigable environment. In variousembodiments, the defined maps may be predefined by and obtained from aremote system. For example, the defined maps may be assembled by theremote system and communicated to the vehicle 100 (wirelessly and/or ina wired manner) and stored in the data storage device 32. Routeinformation may also be stored within data storage device 32—i.e., a setof road segments (associated geographically with one or more of thedefined maps) that together define a route that the user may take totravel from a start location (e.g., the user's current location) to atarget location. As will be appreciated, the data storage device 32 maybe part of the controller 34, separate from the controller 34, or partof the controller 34 and part of a separate system.

The controller 34 includes at least one processor 44 and acomputer-readable storage device or media 46. The processor 44 may beany custom-made or commercially available processor, a centralprocessing unit (CPU), a graphics processing unit (GPU), an applicationspecific integrated circuit (ASIC) (e.g., a custom ASIC implementing aneural network), a field programmable gate array (FPGA), an auxiliaryprocessor among several processors associated with the controller 34, asemiconductor-based microprocessor (in the form of a microchip or chipset), any combination thereof, or generally any device for executinginstructions. The computer readable storage device or media 46 mayinclude volatile and nonvolatile storage in read-only memory (ROM),random-access memory (RAM), and keep-alive memory (KAM), for example.KAM is a persistent or non-volatile memory that may be used to storevarious operating variables while the processor 44 is powered down. Thecomputer-readable storage device or media 46 may be implemented usingany of a number of known memory devices such as PROMs (programmableread-only memory), EPROMs (electrically PROM), EEPROMs (electricallyerasable PROM), flash memory, or any other electric, magnetic, optical,or combination memory devices capable of storing data, some of whichrepresent executable instructions, used by the controller 34 incontrolling the vehicle 100. In various embodiments, controller 34 isconfigured to implement a mapping system as discussed in detail below.

The instructions may include one or more separate programs, each ofwhich includes an ordered listing of executable instructions forimplementing logical functions. The instructions, when executed by theprocessor 44, receive and process signals (e.g., sensor data) from thesensor system 28, perform logic, calculations, methods and/or algorithmsfor automatically controlling the components of the vehicle 100, andgenerate control signals that are transmitted to the actuator system 30to automatically control the components of the vehicle 100 based on thelogic, calculations, methods, and/or algorithms. Although only onecontroller 34 is shown in FIG. 1, embodiments of the vehicle 100 mayinclude any number of controllers 34 that communicate over any suitablecommunication medium or a combination of communication mediums and thatcooperate to process the sensor signals, perform logic, calculations,methods, and/or algorithms, and generate control signals toautomatically control features of the vehicle 100.

The communication system 36 is configured to wirelessly communicateinformation to and from other entities 48, such as but not limited to,other vehicles (“V2V” communication), infrastructure (“V2I”communication), networks (“V2N” communication), pedestrian (“V2P”communication), remote transportation systems, and/or user devices. Inan exemplary embodiment, the communication system 36 is a wirelesscommunication system configured to communicate via a wireless local areanetwork (WLAN) using IEEE 802.11 standards or by using cellular datacommunication. However, additional or alternate communication methods,such as a dedicated short-range communications (DSRC) channel, are alsoconsidered within the scope of the present disclosure. DSRC channelsrefer to one-way or two-way short-range to medium-range wirelesscommunication channels specifically designed for automotive use and acorresponding set of protocols and standards.

In accordance with various embodiments, controller 34 may implement anautonomous driving system (ADS) 70 as shown in FIG. 2. That is, suitablesoftware and/or hardware components of controller 34 (e.g., processor 44and computer-readable storage device 46) may be utilized to provide anautonomous driving system 70 that is used in conjunction with vehicle100.

In various embodiments, the instructions of the autonomous drivingsystem 70 may be organized by function or system. For example, as shownin FIG. 2, the autonomous driving system 70 can include a perceptionsystem 74, a positioning system 76, a path planning system 78, and avehicle control system 80. As can be appreciated, in variousembodiments, the instructions may be organized into any number ofsystems (e.g., combined, further partitioned, etc.) as the disclosure isnot limited to the present examples.

In various embodiments, the perception system 74 synthesizes andprocesses the acquired sensor data and predicts the presence, location,classification, and/or path of objects and features of the environmentof the vehicle 100. In various embodiments, the perception system 74 canincorporate information from multiple sensors (e.g., sensor system 28),including but not limited to cameras, lidars, radars, and/or any numberof other types of sensors. In various embodiments, all or parts of theradar detections may be included within the perception system 74.

The positioning system 76 processes sensor data along with other data todetermine a position (e.g., a local position relative to a map, an exactposition relative to a lane of a road, a vehicle heading, etc.) of thevehicle 100 relative to the environment. As can be appreciated, avariety of techniques may be employed to accomplish this localization,including, for example, simultaneous localization and mapping (SLAM),particle filters, Kalman filters, Bayesian filters, and the like.

The path planning system 78 processes sensor data along with other datato determine a path for the vehicle 100 to follow. The vehicle controlsystem 80 generates control signals for controlling the vehicle 100according to the determined path.

In various embodiments, the controller 34 implements machine learningtechniques to assist the functionality of the controller 34, such asfeature detection/classification, obstruction mitigation, routetraversal, mapping, sensor integration, ground-truth determination, andthe like.

In various embodiments, the positioning system 76 is configured todetermine where the vehicle 100 is located or positioned within the grid(i.e. occupancy grid), and the dynamic object detections determine wheremoving objects are located relative to the vehicle 100 within the grid(not shown). Sensor input from the sensing devices 40 a-40 n may beprocessed by the maneuver risk planning module 302 for making thesedeterminations. Also, in some embodiments, the vehicle positioningsystem 76 and/or the path planning system 78 communicate with the otherentities to determine the relative positions of the vehicle 100 and thesurrounding vehicles, pedestrians, cyclists, and other dynamic objects.

More specifically, the sensor input to the risk maneuver planning module302 may be radar-based and/or laser-based (lidar) detections from one ormore of the sensing devices 40 a-40 n. The maneuver risk planning module302 may filter and determine which of the detections are dynamic objects(moving objects that are actually on the road).

The maneuver risk planning module 302 may process this information andgenerate a Markov random field (MRF) (i.e., Markov network, undirectedgraphical model, etc.) to represent the dependencies therein. Using thisinformation, and using a reinforcement training process, the maneuverrisk planning module 302 may determine (i.e., predict) the riskassociated with initiating (i.e., executing) a particular maneuver(e.g., a right turn into cross traffic).

From that prediction function, the maneuver risk planning module 302 maydetermine the degree to which individual cells influence the riskprediction output. In some embodiments, the maneuver risk planningmodule 302 may identify which of the sensing devices 40 a-40 n have themost influence on the maneuver risk prediction, and those sensingdevices 40 a-40 n may correlate to certain ones of the cells. The cellsthat are identified as having higher influence on risk prediction areidentified by the maneuver risk planning module 302 as being morerelevant than the others.

FIG. 3 is a high level block diagram depicting an example maneuver riskplanning module 302 for providing action risk assessment and objectextraction data for use within behavior and perception systems of avehicle, in accordance with an embodiment. The example maneuver riskplanning module 302 is configured to apply one or more tracking andestimation modules to a plurality of radar scan detections from a radar305 (with n scans (ΔT)) for target detection with pre-filtering senseddata from a prefiltering module 310 to configure an occupancy velocitybelief state density grid.

In various exemplary embodiments, the maneuver risk planning moduledetermines the relevance uncertainty indicators (i.e. also known asuncertainty factors) that include relevance uncertainty indicators forthe different areas. This includes processing the sensor data to:recognize a current situation of the vehicle and accordinglypre-decision predicting the uncertainty indicators for assessing therisk of executing a particular vehicle maneuver; determine inpre-decision planning the degree of influence that a particularuncertainty indicator has on the different areas on the prediction; andcalculate the maneuver risk indicator for the different areas accordingto the determined degree of influence, including calculating higherlevels of maneuver risk indicators for areas having higher degrees ofinfluence. Also, the control command from the AV perception module 335includes generating the control command for the maneuver as a functionof the uncertainty indicator and the maneuver risk relevance factor.

The maneuver risk planning module 302 is programmed to generate a Markovrandom field (MRF) from a Markov Random Field module 315 to recognizethe current situation and for applying a belief state Markov model todetermined uncertainty indicators over the target (i.e. cross-traffic)objects from the radar scan data detections received over a prescribedperiod of time. The plurality of modules in the example maneuver riskplanning module 302 includes the Markov Random Field (MRF) module 315for recognizing the current situations, for generating occupancyvelocity grid data, and for populating cells of the occupancy velocitygrid. The action risk assessment module 320 receives data in cells ofthe occupancy velocity and generates uncertainty indicators for actionsmapped to the cells for action-risk mapping and pre-decision making. Theobject extraction module 325 generates object tracks based on data fromcells populated in the occupancy velocity grid for common areas ofperception from the radar sensor 305 input to provide object track datacorresponding to the relevance uncertainty indicators for pre-decisionmaking for different areas within the perception.

The example pre-filtering module 310 further includes a pre-filteringmodule 310 configured to select the one or more mapping and gatingmodules to apply to determine which of a plurality of radar targetdetections is a likely true detection. The example maneuver riskplanning module 302 is further configured to output the likely detectiondetermined by the pre-filtering module 310. The radar detections for themaneuver risk planning module 302 may be implemented on the controller34 of FIG. 1, or on a separate controller, or on a combination ofcontrollers, in various embodiments.

In FIG. 4, the prefiltering module 310, performs map filtering steps ofsensed data from road data, and road speed data from the radar scandetections from the radar 305 (with n scans (ΔT)) to configure anoccupancy velocity belief state density grid. The map matching module410 is where scan detections are mapped between the sensor domain andthe map domain.

Referring to FIG. 4, the example map matching module 410 is configuredto select the more likely true detection of target 421 between multiplefalse/true radar candidate detections by selecting a candidate detectionthat is closest to a known target path and less than some maximumdistance threshold distance away from the target path. The selectionmodule (road speed filter 418) may choose the map matching module 410 toselect the more likely true detection of target 421 if a map of the areain the vicinity of the vehicle is available and/or alternate true/falsedetection methods are not available.

The example map matching module 410 is configured to use the position ofa road on an area map to gain knowledge of valid target positions, if anarea map is available. The example map matching module 410 is alsoconfigured to infer a valid target position from camera data, path plandata, map data (e.g., from lane, curb detection, target trails). Theexample map matching module 410 is configured to select the candidatedetection that is closest to a known target path (e.g., middle of theroad) and less than a maximum threshold distance (as determined from acalculated function of angle and distance from host to target point)away from the target path (i.e. the center line of the road). The mapcould be a detailed previously existing map or a coarse map regionderived from detection results from an imaging device such as a camera.

After obtaining the road data, and a speed estimation of the vehicle andtarget moving targets, map matching can be applied to target position ameasurement of distance between the projected points (i.e. true movingtargets), P₁, P₂, P₃ in a radius range defined by a circle 412 of eachof the points P₁ to P₃. A determination of target points (in this caseP₁ to P₃) detected within a map matching area by the pre-filteringmodule 310 of false detections 415 and true detections 420 based on theexpected speed, the direction of the moving target and assumptions ofdriving around the center of the lane. That is, the pre-filtering modulecalculates the apparent static range of the target and the measuredvelocity of the target on the road to make the determinations of a falsedetection 415 or a true detection 420.

The example false detections and true detection on the map module 308 isconfigured, at discrete instances in time, to: (a) calculate theapparent static range rate (srr) of each target; (b) calculate the roadspeed of each target, assuming the target is traveling on the road; and(c) filter out (e.g., reject) candidate detections that are inconsistentwith a target travelling on the road. In this example, the target 416,at this time instance, is not positioned on the road and its associatedcandidate detection is filtered out. In the example, the target 421, atthis time instance, is positioned in a lane on the road, its directionof travel is consistent with the direction of travel for the lane, andits speed is consistent with the speed limit for the lane. Consequently,the candidate detection associated with target 421, at this instance intime, is not filtered out.

After making this determination, the predicted position by track gatingmodule 425 (or batch gating) of the received sensed data, a predictedposition based on the last position is calculated. The example trackgating module 425 is configured to select the more likely true detectionin circle 412 between multiple uncertain candidate detections bychoosing a candidate that is closest to a predicted target position as atrue radar detection. The map matching module 410 may choose the trackgating module 425 if radar tracks already exist for a target and/oralternate tracking and estimation methods are not available.

The example track gating module 425 is configured to (a) compute, foreach existing radar track, a distance metric to each true candidatedetection (i.e. P₁, P₂, P₃); (b) predict a next position of a targetposition using a prediction filter (e.g., a Kalman filter); and (c)select the true candidate detections (i.e. target 421) that is closestto the predicted next position and less than a threshold distance awayfrom the predicted next position. The distance metric can include, butis not limited to, the Euclidian distance or the Mahalanobis distance;the latter of which can be calculated with knowledge of the predictedposition covariance obtained from a Kalman filter.

In one example, the track gating module 425 may choose a candidate thatis closest to a predicted target position 401 for a radar detection bycomputing, for each existing radar track, a distance metric from thelast position 403 of a tracked target to each candidate detection;applying a Kalman filter to predict the next position of the trackedtarget and converting the predicted next position to the measurementplane (e.g., for linear KF, y⁻=HF{circumflex over (x)}); computingsquared Mahalanobis distance d_(k) ²=(y⁻)^(T)S⁻¹y⁻ where S=R+HP⁻H^(T)for candidate detection k∈{1,2, . . . } (can alternatively use Euclidiandistance); and, for each existing radar track 411, gating the candidatedetection, using the knowledge that d_(k) ² is chi-squared distributedto pick a threshold T for a percentile P (e.g., 95%), and associatingdetection k with the track if d_(k) ²<T. In the event that multipleuncertain detections fall within the gate 405, the detection with theshortest Mahalanobis distance is chosen.

The example track gating module 425 is configured to access a predictionfilter (e.g., a Kalman filter) to obtain a predicted location 401 for atarget based on the last position 403 for the target. Using thepredicted location 401, the example track gating module 425 isconfigured to identify a gate region 405 (e.g., apply a gate) that iswithin a threshold distance away from the next predicted location 401.The example track gating module 425 is configured to select the likelytrue detection by selecting the nearest true candidate detection (e.g.,candidate detection for target 421) that is within the gate region 405as the likely true detection.

FIG. 5 illustrates a diagram of the Markov Random Field (MRF) module forrecognizing the current situation and applying a belief state Markovmodel to determined uncertainty indicators over the target (i.e.cross-traffic) objects from the radar scan data detections which havebeen received over a prescribed period of time, in accordance with anembodiment. The MRF 315 module (i.e., Markov network, undirectedgraphical model, etc.) to represent the dependencies for the uncertaintyindicators for pre-decision planning. Using this information, and usinga reinforcement training process, the maneuver risk planning module 302may determine (i.e., predict) the risk associated with initiating (i.e.,executing) a particular maneuver (e.g., a left or right turn into crosstraffic).

In FIG. 5, the Markov Random Field (MRF) module 315 receives filteredstatic and dynamic targets that have been clustered into dynamic objectsby clustering and module 505 (of FIG. 5) for recognizing the currentsituations, for generating occupancy velocity grid data, and forpopulating cells of the occupancy velocity grid. The MRK module 315 in ameasurement grid aggregation module 510 fills in a measurement grid withdynamic objects from the clustering step. The measurement gridaggregation module 510 cycles through a feedback loop of processingsteps to fill each available cell of the grid in an incremental step bystep manner until all the pipeline of each dynamic objects received isexhausted or the grid is deemed sufficiently filled (i.e. in accordancewith a prescribed time period) or until completion. The cyclicprocessing steps for each dynamic object includes calculating the objectmeasurement density 515, spreading the density over 2D cell windows 520,spreading the velocities over the same 2D cell windows 525 and addingthe calculated spread densities, velocities of each object to the grid530, and repeating the cycle until completion, until the grid is full,or until deemed sufficiently complete. Once the dynamic objects areadded to the grid, the densities are summed by summing module 540 by acalculation function. Next, by a calculation function, the velocitiesare averaged or weighted by factors across the cells of the cell windowby a weighted average velocity module 545 for density-velocitymeasurement grid representations.

In various exemplary embodiments, a Bayesian update 550 is provided forthe example density and velocity modelling on the measurementaggregation grid for each dynamic object. The Bayesian update 550includes update velocity based on Bayesian occupancy weighting 560 frominput of an update occupancy gird probabilities module 555 calculation

In some embodiments, the MRF module 315 calculate the uncertainty datafor the cells 552) using one or more Bayesian algorithms. Thecalculations are used to quantify, for different cells 552, expectederror (i.e., information gain) computed as true occupancy (Ø∈{0,1}),minus an estimate (p) squared, multiplied by probability with respect tooccupancy. In this context, occupancy grid algorithms are used tocompute approximate posterior estimates for these random variables.Stated differently, expected prediction error (i.e., the uncertaintyfactor) calculated by the update occupancy grid probabilities module 555for each grid cell may be calculated according to the following equation(1):

E[(Ø−p)²]=Σ_(Ø∈{0,1})(Ø−p)² P(Ø)=(0−p)²(1−p)+(1−p)² p=p(1−p)

wherein Ø represents true occupancy, p represents the estimate, and Prepresents probability.

In addition, a Bayesian update 550 may be performed for a given cell 552according to the following equation (2):

$p_{post} = \frac{\left( {1 - a} \right)^{({n - k})}a^{k}p}{{\left( {1 - a} \right)^{({n - k})}a^{k}p} + {\left( {1 - b} \right)^{({n - k})}{b^{k}\left( {1 - p} \right)}}}$

wherein n represents the number of observations at a given cell, krepresents the number of detections, a represents detections (P), brepresents false-alarm (P), and p represents occupancy (P).

Accordingly, the perception module 335 (of FIG. 3) may calculateposteriors for a given cell 552 according to equation (2). Additionally,the perception module 335 may calculate the expected future uncertaintyfor the cells 552 for use in risk assessments according to the followingequation (3):

E[RMSE]=√{square root over (p(1−p))} (√{square root over (ab)}+√{squareroot over ((1−a)(1−b)))}^(n)

Thus, the perception module 335 may create a heuristic model which canbe used for compensating for uncertainties and adaptively controllingthe sensing devices 40 a-42 n. For a given cell 552 within the grid, theperception module 335 determines how much uncertainty will be increasedor reduced if one or more sensing devices 40 a-42 n were used to thecorresponding physical space in the environment defined by the occupancygrid. In some embodiments, the adaptive sensor control system 34 relieson this information (uncertainty reduction in the cells 552) whengenerating sensor control commands to the sensing devices 42 a-42 n.

FIG. 6 illustrates a diagram of an action risk assessment module forprocessing data from cells of the occupancy velocity grid in accordancewith an embodiment. In FIG. 6, the action risk assessment module 320receives data in cells of the occupancy velocity and applies mappingfunctions with uncertainty indicators for action candidates for use inaction-risk mapping and pre-decision making.

The example action risk assessment module 320 in FIG. 6 is configured toselect the more likely true action candidates by applying a mappingfunction 610 based on the sensed occupancy velocity data 605 from theoccupancy velocity grid 600. Each sensed occupancy velocity data 605from each cell when applying a mapping function 610 is capable ofgeneration multiple actions candidate 620. Using a trained machinelearning (ML) model 615 with risk vectors 625 for each of the multipleaction candidates 620 pre-decision maneuver behavior can be modeled. Forexample, the action candidates 620 can include straight, right and leftpre-decision maneuvers in the trained ML model 615. The ML model 615 isconfigured to predict drivable paths, determine which of the riskvectors contributes to the action candidates to predict the drivablepaths, and select candidate actions that contribute to the drivablepaths as the more likely true action candidates.

Also, off-line semi-supervised re-enforcement learning can be modeled byan example reinforcement learning off-line training module 640contributing to the action risk mapping with historical, stored data orcloud data from labels form related video of the locality of interestand corresponding sensor data. The example reinforcement learningoff-line training module 640 includes a trained ML model that wastrained to predict future traversable paths through traffic using actioncandidate detections. The example reinforcement learning (discriminator)off-line module 640 is configured to collect labels 645 for determiningmaneuvers such as “it is safe to turn left?”; to co-collect occupancygrids (MRF) 650 for applying mapping functions 610 to generate actioncandidates off-line, to extract features from grids 655 to the trainedML model to predict drivable paths, and to apply support machine vectors(SVM) and Gaussian Process 660 for recognizing class patterns of actioncandidates to determine which of the action candidates contribute to thedrivable paths, and select action candidates that contribute to thedrivable paths as the more likely true action candidates for sending tothe ML model 615 enabling learned mapping functions to be applied in theML model 615 . By applying both labeling and risk vector indicators, thebehavior module 630 can collect behavior labels such as “is it safe todo behavior B?” . . . etc. in real-time.

In various exemplary embodiments, a collected labeled data set ofhistorical radar data such as velocity data and drivable paths that canbe generated from data concerning prior paths driven by the vehicle. Thehistorical sensor data can be used to train using reinforced learningtechniques, the ML model 615 to predict drivable paths based on actioncandidate detections. After the ML model 615 is trained to predictdrivable paths based on action candidate detections, real-time sensordata can be applied to the ML model.

FIG. 7 illustrates a diagram of the object extraction module forgenerating object tracks for pre-decision making by the risk maneuverplanning module, in accordance with an embodiment. The object extractionmodule 325 (of FIG. 7) generates object tracks based on data from cellspopulated in the occupancy velocity grid 710 for areas of perceptionfrom sensed data to provide object track data corresponding with therelevance uncertainty indicators represented for different areas withina perception grid for publishing. The occupancy velocity grid 710 isconfigured with an adaptive threshold of the occupancy density cells 715by receiving threshold 718 inputs to adaptively configure the thresholdoccupancy cell densities to ensure that the uncertainty indicators areeffectively incorporated in the occupancy velocity grid 710. That is,high probability locations use a bottom up physical based clustering 720and were velocity cluster centers can be associated 725 with objecttracks 730 so that regions with high uncertainty can be distinguishedfrom those with low uncertainty. The process includes feedback of priordetected velocity clusters 727 for efficiency. The object tracks 730resulting are published in object representations for ML models ofvehicle perception.

FIG. 8 is a process flow chart depicting an example process for maneuverrisk assessments with uncertainties to predict drivable paths based onmultiple radar candidate detections, in accordance with variousembodiments.

In the flowchart of FIG. 8, at task 810 a number of radar scans of atime period generate sensed data for processing for receipt. It iscontemplated that a variety of sensor devices including near distance,long distance, image, light, acoustic and IR sensing devices may be usedor incorporated for generating the sensed data sets, and the disclosureis not limited to radar data.

Next, at task 820 processing steps for mapping and tracking of dynamictargets are performed. The prefiltering module (i.e. 310 of FIG. 4),performs map filtering steps of sensed data from road data, and roadspeed data from the radar scan detections from the radar (with n scans(ΔT)) for configuring an occupancy velocity belief state density gridrepresenting regions of interest about the vehicle. The example mapmatching module selects the more likely true detection of target betweenmultiple false/true radar candidate detections by selecting a candidatedetection that is closest to a known target path and less than somemaximum distance threshold distance away from the target path. Theselection module may choose the map matching module for selecting themore likely true detection of target if a map of the area in thevicinity of the vehicle is available and/or alternating true/falsedetection methods are not available. The example map matching moduleuses the position of a road on an area map to gain knowledge of validtarget positions, if an area map is available. The example map matchingmodule is also configured to infer a valid target position from cameradata, path plan data, map data (e.g., from lane, curb detection, targettrails). The example map matching module is configured to select thecandidate detection that is closest to a known target path (e.g., middleof the road) and less than a maximum threshold distance (as determinedfrom a calculated function of angle and distance from host to targetpoint) away from the target path (i.e. the center line of the road). Themap could be a detailed previously existing map or a coarse map regionderived from detection results from an imaging device such as a camera.

At task 830, the MRK algorithm is used for generating a grid baseddynamic model to determine uncertainty indicators over the target (i.e.cross-traffic) objects. The MRF (i.e. 315 of FIG. 5) determines (i.e.,predict) the risk associated with initiating (i.e., executing) aparticular maneuver (e.g., a left or right turn into cross traffic). Theprocessing steps include the Markov Random Field (MRF) module receivingfiltered static and dynamic targets for recognizing the currentsituations, for generating occupancy velocity grid data, and forpopulating cells of the occupancy velocity grid; applying a measurementgrid aggregation module for filling in a measurement grid with dynamicobjects from the clustering step. A feedback loop of processing steps tofill each available cell of the grid in an incremental step by stepmanner until all the pipeline of each dynamic objects received isexhausted or the grid is deemed sufficiently filled (i.e. in accordancewith a prescribed time period) or until completion. The cyclicprocessing steps for each dynamic object includes calculating the objectmeasurement density, spreading the density over 2D cell windows,spreading the velocities over the same 2D cell windows and adding thecalculated spread densities, velocities of each object to the grid, andrepeating the cycle until completion, until the grid is full, or untildeemed sufficiently complete. Once the dynamic objects are added to thegrid, the densities are summed; and the velocities are averaged orweighted by factors across the cells for density-velocity measurementgrid representations. Also, a Bayesian update is provided for theexample density and velocity modelling and includes update velocitiesbased on Bayesian occupancy weighting.

At task 840, an action risk assessment using semi-supervised ML modeltraining with off-line training is performed. The example action riskassessment module selects the more likely true action candidates byapplying a mapping function based on the sensed occupancy velocity dataand then applies a trained machine learning (ML) model with risk vectorsfor each of the multiple action candidates so that pre-decision maneuverbehavior can be modeled. For example, the action candidates can includestraight, right and left pre-decision maneuvers in the trained ML model.The ML model predicts drivable paths, determines which of the riskvectors contributes to the action candidates to predict the drivablepaths, and select candidate actions that contribute to the drivablepaths as the more likely true action candidates for the behaviormodeling 850.

Also, tasks, at 840, related to off-line semi-supervised re-enforcementlearning are modeled by an example reinforcement learning off-linetraining module contributing to the action risk mapping with historical,stored data or cloud data from labels form related video of the localityof interest and corresponding sensor data. The example reinforcementlearning off-line trained ML model predicts future traversable pathsthrough traffic using action candidate detections by collecting labelsfor determining maneuvers such as “it is safe to turn left?”;co-collecting occupancy grids (MRF) for applying mapping functions togenerate action candidates off-line, extracting features from grids fortraining the ML model to predict drivable paths, and finally, applyingsupport machine vectors (SVM) and Gaussian Process for recognizing classpatterns of action candidates to determine which of the actioncandidates contribute to the drivable paths for the behavior modeling850.

At task 845, the processing steps for object extraction using adaptivethreshold of occupancy grid densities for object based representationsis performed. The object extraction module (i.e. 325 of FIG. 7) performsprocessing steps of generating object tracks based on data from cellspopulated in the occupancy velocity grid for areas of perception fromsensed data to provide object track data corresponding with therelevance uncertainty indicators represented for different areas withina perception grid for publishing. In other words, a degree of influenceof the different areas by densities of the occupancy grid is determined.The occupancy velocity grid is configured with an adaptive threshold ofthe occupancy density cells by receiving threshold inputs to adaptivelyconfigure the threshold occupancy cell densities to ensure that theuncertainty indicators are effectively incorporated in the occupancyvelocity grid. That is, high probability locations use a bottom upphysical based clustering and were velocity cluster centers can beassociated with object tracks so that regions with high uncertainty canbe distinguished from those with low uncertainty. The process includesfeedback of prior detected velocity clusters for efficiency. The objecttracks resulting from the processing steps are published in objectrepresentations for ML models of vehicle perception 855.

While at least one exemplary embodiment has been presented in theforegoing detailed description, it should be appreciated that a vastnumber of variations exist. It should also be appreciated that theexemplary embodiment or exemplary embodiments are only examples, and arenot intended to limit the scope, applicability, or configuration of thedisclosure in any way. Rather, the foregoing detailed description willprovide those skilled in the art with a convenient road map forimplementing the exemplary embodiment or exemplary embodiments. Variouschanges can be made in the function and arrangement of elements withoutdeparting from the scope of the disclosure as set forth in the appendedclaims and the legal equivalents thereof.

As an example, the apparatus, systems, methods, techniques and articlesdescribed herein may be applied to measurement systems other than radarsystems (for example Lidar, acoustic or image systems). The apparatus,systems, methods, techniques and articles described herein may beapplied to velocity measurement sensors such as laser or light-basedvelocity measurement sensors.

What is claimed is:
 1. A risk maneuver assessment system for planningmaneuvers with uncertainties of a vehicle, comprising: a firstcontroller with a processor programmed to generate a perception of anenvironment of the vehicle and a behavior decision making model for thevehicle including performing a calculation upon a sensor input toprovide, as an output an action risk mapping and at least one targetobject tracking for different areas within the environment of thevehicle; a sensor system configured to provide the sensor input to theprocessor for providing an area in the environment of vehicle forfiltering target objects; one or more modules configured to, by aprocessor, map and track target objects to make a candidate detectionfrom multiple candidate detections of a true candidate detection as thetracked target object; one or more first modules configured to, by theprocessor, apply a Markov Random Field (MRF) algorithm for recognizing acurrent situation of the vehicle in the environment and predict a riskof executing a planned vehicle maneuver at the true detection of thedynamically tracked target; one or more second modules configured to, bythe processor, apply mapping functions to sensed data of the environmentfor configuring a machine learning model of decision making behavior ofthe vehicle; one or more third modules configured to, by the processor,apply adaptive threshold to cells of an occupancy grid configured forrepresenting an area of tracking of objects within the vehicleenvironment; and a second controller with a processor configured togenerate control commands in accordance with modeling of the decisionmaking behavior and the perception of the environment of the vehicle forplanned vehicle maneuvers.
 2. The system of claim 1, further comprising:the one or more first modules programmed to generate a Markov RandomField (MRF) to recognize the current situation.
 3. The system of claim1, further comprising: the one or more of the second and/or thirdmodules further configured to select as the true detection at least oneof the candidate detections that is within a radius for the target, anda candidate detection that is closest to a first known mapped pathway.4. The system of claim 3, further comprising: the one or more of thesecond and/or third modules further configured to select as the truedetection, the candidate that indicates a position and velocity that areconsistent with a target traveling on a second known travel mappedpathway, and a selection as a false detection, the candidate thatindicates a position that is outside the second known pathway or thevelocity is not consistent with the target traveling.
 5. The system ofclaim 4, further comprising: a fourth module configured to compute, by agating operation, a distance metric from the last position of a trackedtarget object to a predicted position less than a threshold distancerelated to one or more of the candidate detections.
 6. The system ofclaim 5, further comprising: one or more fifth modules configured to, bythe processor, apply the Markov Random Field (MRF) algorithmrepresenting the tracked target object in one or more cells of anoccupancy grid by: calculating an object measurement density for eachtracked target object represented in the one or more cells of theoccupancy grid; spreading the density over a window comprising the setof cells of the occupancy grid represented by the tracked target object;and spreading velocities over the window comprising a same set of cellsof the occupancy grid represented by the tracked target object.
 7. Thesystem of claim 6, further comprising: one or more sixth modulesconfigured to, by the processor, apply mapping functions to sensed dataof the environment for configuring a machine learning (ML) model ofdecision making behavior of the vehicle by an action risk assessmentmodel trained using semi-supervised machine learning techniques byon-line and off-line training for mapping function to candidate actionsto determine with risk factors a learned drivable path.
 8. The system ofclaim 7, further comprising: a seventh module configured to, by aprocessor, perform in the off-line training of the ML model comprising:collecting labels, co-collecting occupancy velocity grids, extractingfeatures from the occupancy grids, and applying at least support vectormachine (SVM) techniques for recognizing class patterns of the candidateactions to determine with risk factors the learned drivable path.
 9. Thesystem of claim 8, further comprising an eighth module configured to, bythe processor, apply adaptive threshold to cells of an occupancy gridconfigured for representing area of tracking of objects within thevehicle environment comprising: a ninth module configured to, by theprocessor, compute by an adaptive threshold occupancy density, thelikelihood that a candidate action is available for the target trackedobject based on the computed density distribution, and select thecandidate action that has the highest probability of being available;and a tenth module configured to, by the processor, compute a clusteringfor velocity clusters for a set of candidate actions to select thetarget tracked object that indicates a position that is consistent witha learned drivable path.
 10. The system of claim 8, wherein the ML modelis trained using reinforcement learning techniques using a data set ofpast collected labels and sensor data of drivable paths and wherein theeight module is configured to select the candidate action that willlikely contribute to one of the drivable paths wherein the sensor dataat least comprises one of: radar, acoustic, lidar or image sensor data.11. A vehicle, comprising: a sensor detection sensing device includingone or more of a set comprising: a radar, acoustic, lidar and imagesensing device; a risk maneuver assessment system for assessing one ormore uncertainty factors in planned maneuvers; and a plurality ofmodules configured to, by a processor, generate a perception of anenvironment of the vehicle and a output target output for trackingdifferent areas within the environment; the plurality of modulescomprising: one or more modules configured to, by a processor, map andtrack target objects to make a candidate detection from multiplecandidate detections of a true candidate detection as the tracked targetobject; one or more modules configured to, by the processor, apply aMarkov Random Field (MRF) algorithm for recognizing a current situationof the vehicle in the environment and for predicting a risk of executinga planned vehicle maneuver at the true detection of the dynamicallytracked target; one or more modules configured to, by the processor,apply mapping functions to sensed data of the environment forconfiguring a machine learning model of decision making behavior of thevehicle; one or more modules configured to, by the processor, applyadaptive threshold to cells of an occupancy grid configured forrepresenting areas of tracking of objects within the environment; and acontroller with a processor configured to generate control commands inaccordance with modeling of the decision making behavior and theperception of the environment of the vehicle for planned vehiclemaneuvers.
 12. The system of claim 11, wherein the one or more modulesare programmed to generate a Markov Random Field (MRF) to recognize thecurrent situation.
 13. The system of claim 11, further comprising: theone or more modules configured to select as the true detectioncomprising: a first module configured to select, as the true detection,the candidate detection that is within a radius for the target; and asecond module configured to select, as the true detection, the candidatedetection that is closest to a first known mapped pathway.
 14. Thesystem of claim 13, further comprising: the one or more modules furtherconfigured to select as the true detection comprising: a third moduleconfigured to select the true detection, the candidate that indicates aposition and velocity that are consistent with a target traveling on asecond known travel mapped pathway; and the third module configured toselect a false detection, the candidate that indicates a position thatis outside the second known pathway or the velocity is not consistentwith the target traveling.
 15. The system of claim 14, furthercomprising: the one or more modules further configured to select as thetrue detection comprising: a fourth module configured to compute, by agating operation, a distance metric from the last position of a trackedtarget object to a predicted position less than a threshold distancerelated to one or more of the candidate detections.
 16. The system ofclaim 15, wherein the one or more modules configured by the processorfor applying the Markov Random Field (MRF) algorithm representing thetracked target object in one or more cells of an occupancy grid furthercomprising: a fifth module is configured to calculate an objectmeasurement density for each tracked target object represented in theone or more cells of the occupancy grid; a sixth module is configured tospread the density over a window comprising the set of cells of theoccupancy grid represented by the tracked target object; and a seventhmodule is configured to spread velocities over the window comprising asame set of cells of the occupancy grid represented by the trackedtarget object.
 17. The system of claim 16, wherein the one or moremodules configured by the processor for applying mapping functions tosensed data of the environment for configuring a machine learning (ML)model of decision making behavior of the vehicle further comprising: aneighth module comprising an action risk assessment model trained usingsemi-supervised machine learning techniques, by on-line and off-linetraining, for mapping functions to candidate actions to determine withrisk factors a learned drivable path wherein the eight module in theoff-line training of the ML model comprises: collecting labels,co-collecting occupancy velocity grids, extracting features from theoccupancy grids, and applying at least support vector machine (SVM)techniques for recognizing class patterns of the candidate actions todetermine with risk factors the learned drivable path.
 18. The system ofclaim 17, wherein the one or more modules for applying adaptivethreshold to cells of an occupancy grid configured for representing areaof tracking of objects within the vehicle environment comprising: aninth module configured to compute by an adaptive threshold occupancydensity, the likelihood that a candidate action is available for thetarget tracked object based on the computed density distribution, andselect the candidate action that has the highest probability of beingavailable; and a tenth module configured to compute a clustering forvelocity clusters for a set of candidate actions to select the targettracked object that indicates a position that is consistent with alearned drivable path.
 19. The system of claim 18, wherein the ML modelis trained using reinforcement learning techniques using a data set ofpast collected labels and radar data of drivable paths and wherein theeight module is configured to select the candidate action that willlikely contribute to one of the drivable paths.
 20. A planning system ofa vehicle, the system comprising: a sensor system configured to providethe sensor input to the processor for providing an area in theenvironment of vehicle for filtering target objects; and anon-transitory computer readable medium comprising: a first moduleconfigured to, by a processor, select, as the true detection, thecandidate detection that is within a radius for the target; a secondmodule configured to, by a processor, select, as the true detection, thecandidate detection that is closest to a first known mapped pathway; athird module configured to, by a processor, select the true detection,the candidate that indicates a position and velocity that are consistentwith a target traveling on a second known travel mapped pathway, and thethird module configured to select a false detection, the candidate thatindicates a position that is outside the second known pathway or thevelocity is not consistent with the target traveling; a fourth moduleconfigured to, by a processor, compute, by a gating operation, adistance metric from the last position of a tracked target object to apredicted position less than a threshold distance related to one or moreof the candidate detections; a fifth module is configured to, by aprocessor, calculate an object measurement density for each trackedtarget object represented in the one or more cells of the occupancygrid; a sixth module is configured to, by a processor, spread thedensity over a window comprising the set of cells of the occupancy gridrepresented by the tracked target object; a seventh module is configuredto, by a processor, spread velocities over the window comprising a sameset of cells of the occupancy grid represented by the tracked targetobject; an eighth module comprising an action risk assessment modeltrained using semi-supervised machine learning techniques by on-line andoff-line training for mapping function to candidate actions to determinewith risk factors a learned drivable path wherein the eighth module inthe off-line training of the ML model is configured to, by a processor,collect labels, co-collect occupancy velocity grids, extract featuresfrom the occupancy grids, and apply at least support vector machine(SVM) techniques for recognizing class patterns of the candidate actionsto determine with risk factors the learned drivable path; a ninth moduleconfigured to, by a processor, compute by an adaptive thresholdoccupancy density, the likelihood that a candidate action is availablefor the target tracked object based on the computed densitydistribution, and select the candidate action that has the highestprobability of being available; and a tenth module configured to, by aprocessor, compute a clustering for velocity clusters for a set ofcandidate actions to select the target tracked object that indicates aposition that is consistent with a learned drivable path wherein the MLmodel is trained using reinforcement learning techniques using a dataset of past collected labels and radar data of drivable paths andwherein the eight module is configured to select the candidate actionthat will likely contribute to one of the drivable paths.